






import math
from typing import Any, Callable, List, Optional, Tuple, Union

import numpy as np
import torch

from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor


if is_onnx_available():
						from ..onnx_utils import OnnxRuntimeModel

from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
from .continous_encoder import SpectrogramContEncoder
from .notes_encoder import SpectrogramNotesEncoder


__UpperCamelCase   :   Dict      =    logging.get_logger(__name__)  # pylint: disable=invalid-name

__UpperCamelCase   :   List[Any]      =    256


class 							__UpperCamelCase     (  _lowerCAmelCase ):
	__snake_case						:List[str] =							['melgan']







	def __init__(	self   :				Dict							,					_lowerCAmelCase   :				SpectrogramNotesEncoder							,					_lowerCAmelCase   :				SpectrogramContEncoder							,					_lowerCAmelCase   :				TaFilmDecoder							,					_lowerCAmelCase   :				DDPMScheduler							,					_lowerCAmelCase   :				OnnxRuntimeModel if is_onnx_available() else Any							,					)		->			None:
		"""simple docstring"""







		super().__init__()

		# From MELGAN
		__lowercase   							=	math.log(1e-5      )  # Matches MelGAN training.
		__lowercase   							=	4.0  # Largest value for most examples
		__lowercase   							=	128

		self.register_modules(
		    notes_encoder=_lowerCAmelCase							,					continuous_encoder=_lowerCAmelCase							,					decoder=_lowerCAmelCase							,					scheduler=_lowerCAmelCase							,					melgan=_lowerCAmelCase							,					)







	def 			_a (	self   :				List[str]							,					_lowerCAmelCase   :				Union[str, Any]							,					_lowerCAmelCase   :				Tuple=(-1.0, 1.0)							,					_lowerCAmelCase   :				Optional[Any]=False      )		->			Optional[Any]:
		"""simple docstring"""







		__lowercase							,    __lowercase   							=	output_range
		if clip:
			__lowercase   							=	torch.clip(_lowerCAmelCase							,					self.min_value							,					self.max_value      )
		# Scale to [0, 1].
		__lowercase   							=	(features - self.min_value) / (self.max_value - self.min_value)
		# Scale to [min_out, max_out].
		return zero_one * (max_out - min_out) + min_out







	def 			_a (	self   :				str							,					_lowerCAmelCase   :				Optional[int]							,					_lowerCAmelCase   :				Optional[Any]=(-1.0, 1.0)							,					_lowerCAmelCase   :				Tuple=False      )		->			List[str]:
		"""simple docstring"""







		__lowercase							,    __lowercase   							=	input_range
		__lowercase   							=	torch.clip(_lowerCAmelCase							,					_lowerCAmelCase							,					_lowerCAmelCase      ) if clip else outputs
		# Scale to [0, 1].
		__lowercase   							=	(outputs - min_out) / (max_out - min_out)
		# Scale to [self.min_value, self.max_value].
		return zero_one * (self.max_value - self.min_value) + self.min_value







	def 			_a (	self   :				str							,					_lowerCAmelCase   :				str							,					_lowerCAmelCase   :				int							,					_lowerCAmelCase   :				str      )		->			Optional[int]:
		"""simple docstring"""







		__lowercase   							=	input_tokens > 0
		__lowercase							,    __lowercase   							=	self.notes_encoder(
		    encoder_input_tokens=_lowerCAmelCase							,					encoder_inputs_mask=_lowerCAmelCase      )

		__lowercase							,    __lowercase   							=	self.continuous_encoder(
		    encoder_inputs=_lowerCAmelCase							,					encoder_inputs_mask=_lowerCAmelCase      )

		return [(tokens_encoded, tokens_mask), (continuous_encoded, continuous_mask)]







	def 			_a (	self   :				Dict							,					_lowerCAmelCase   :				Any							,					_lowerCAmelCase   :				List[Any]							,					_lowerCAmelCase   :				List[Any]      )		->			Dict:
		"""simple docstring"""







		__lowercase   							=	noise_time
		if not torch.is_tensor(_lowerCAmelCase      ):
			__lowercase   							=	torch.tensor([timesteps]							,					dtype=torch.long							,					device=input_tokens.device      )
		elif torch.is_tensor(_lowerCAmelCase      ) and len(timesteps.shape      ) == 0:
			__lowercase   							=	timesteps[None].to(input_tokens.device      )

		# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
		__lowercase   							=	timesteps * torch.ones(input_tokens.shape[0]							,					dtype=timesteps.dtype							,					device=timesteps.device      )

		__lowercase   							=	self.decoder(
		    encodings_and_masks=_lowerCAmelCase							,					decoder_input_tokens=_lowerCAmelCase							,					decoder_noise_time=_lowerCAmelCase      )
		return logits







	@torch.no_grad()
	def __call__(	self   :				Tuple							,					_lowerCAmelCase   :				List[List[int]]							,					_lowerCAmelCase   :				Optional[torch.Generator] = None							,					_lowerCAmelCase   :				int = 100							,					_lowerCAmelCase   :				bool = True							,					_lowerCAmelCase   :				str = "numpy"							,					_lowerCAmelCase   :				Optional[Callable[[int, int, torch.FloatTensor], None]] = None							,					_lowerCAmelCase   :				int = 1							,					)		->			Union[AudioPipelineOutput, Tuple]:
		"""simple docstring"""







		if (callback_steps is None) or (
		    callback_steps is not None and (not isinstance(_lowerCAmelCase							,					_lowerCAmelCase      ) or callback_steps <= 0)
		):
			raise ValueError(
			    F'`callback_steps` has to be a positive integer but is {callback_steps} of type'
			    F' {type(_lowerCAmelCase      )}.'      )

		__lowercase   							=	np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims]							,					dtype=np.floataa      )
		__lowercase   							=	np.zeros([1, 0, self.n_dims]							,					np.floataa      )
		__lowercase   							=	torch.ones((1, TARGET_FEATURE_LENGTH)							,					dtype=_lowerCAmelCase							,					device=self.device      )

		for i, encoder_input_tokens in enumerate(_lowerCAmelCase      ):
			if i == 0:
				__lowercase   							=	torch.from_numpy(pred_mel[:1].copy()      ).to(
				    device=self.device							,					dtype=self.decoder.dtype      )
				# The first chunk has no previous context.
				__lowercase   							=	torch.zeros((1, TARGET_FEATURE_LENGTH)							,					dtype=_lowerCAmelCase							,					device=self.device      )
			else:
				# The full song pipeline does not feed in a context feature, so the mask
				# will be all 0s after the feature converter. Because we know we're
				# feeding in a full context chunk from the previous prediction, set it
				# to all 1s.
				__lowercase   							=	ones

			__lowercase   							=	self.scale_features(
			    _lowerCAmelCase							,					output_range=[-1.0, 1.0]							,					clip=_lowerCAmelCase      )

			__lowercase   							=	self.encode(
			    input_tokens=torch.IntTensor([encoder_input_tokens]      ).to(device=self.device      )							,					continuous_inputs=_lowerCAmelCase							,					continuous_mask=_lowerCAmelCase							,					)

			# Sample encoder_continuous_inputs shaped gaussian noise to begin loop
			__lowercase   							=	randn_tensor(
			    shape=encoder_continuous_inputs.shape							,					generator=_lowerCAmelCase							,					device=self.device							,					dtype=self.decoder.dtype							,					)

			# set step values
			self.scheduler.set_timesteps(_lowerCAmelCase      )

			# Denoising diffusion loop
			for j, t in enumerate(self.progress_bar(self.scheduler.timesteps      )      ):
				__lowercase   							=	self.decode(
				    encodings_and_masks=_lowerCAmelCase							,					input_tokens=_lowerCAmelCase							,					noise_time=t / self.scheduler.config.num_train_timesteps							,					)

				# Compute previous output: x_t -> x_t-1
				__lowercase   							=	self.scheduler.step(_lowerCAmelCase							,					_lowerCAmelCase							,					_lowerCAmelCase							,					generator=_lowerCAmelCase      ).prev_sample

			__lowercase   							=	self.scale_to_features(_lowerCAmelCase							,					input_range=[-1.0, 1.0]      )
			__lowercase   							=	mel[:1]
			__lowercase   							=	mel.cpu().float().numpy()

			__lowercase   							=	np.concatenate([full_pred_mel, pred_mel[:1]]							,					axis=1      )

			# call the callback, if provided
			if callback is not None and i % callback_steps == 0:
				callback(_lowerCAmelCase							,					_lowerCAmelCase      )

			logger.info("""Generated segment"""							,					_lowerCAmelCase      )

		if output_type == "numpy" and not is_onnx_available():
			raise ValueError(
			    """Cannot return output in 'np' format if ONNX is not available. Make sure to have ONNX installed or set 'output_type' to 'mel'."""      )
		elif output_type == "numpy" and self.melgan is None:
			raise ValueError(
			    """Cannot return output in 'np' format if melgan component is not defined. Make sure to define `self.melgan` or set 'output_type' to 'mel'."""      )

		if output_type == "numpy":
			__lowercase   							=	self.melgan(input_features=full_pred_mel.astype(np.floataa      )      )
		else:
			__lowercase   							=	full_pred_mel

		if not return_dict:
			return (output,)

		return AudioPipelineOutput(audios=_lowerCAmelCase      )






'''simple docstring'''

import os

SCREAMING_SNAKE_CASE__     :						Optional[Any]	=  {'''I''': 1, '''V''': 5, '''X''': 1_0, '''L''': 5_0, '''C''': 1_0_0, '''D''': 5_0_0, '''M''': 1_0_0_0}






def 				a  (     UpperCamelCase_    :    str			)       ->	int:
		snake_case__	     =0

		snake_case__	     =0
		while index < len(UpperCamelCase_			) - 1:
				snake_case__	     =SYMBOLS[numerals[index]]
				snake_case__	     =SYMBOLS[numerals[index + 1]]
				if current_value < next_value:
						total_value -= current_value
				else:
						total_value += current_value
				index += 1
		total_value += SYMBOLS[numerals[index]]

		return total_value






def 				a  (     UpperCamelCase_    :    int			)       ->	str:
		snake_case__	     =''

		snake_case__	     =num // 1000
		numerals += m_count * "M"
		num %= 1000

		snake_case__	     =num // 100
		if c_count == 9:
				numerals += "CM"
				c_count -= 9
		elif c_count == 4:
				numerals += "CD"
				c_count -= 4
		if c_count >= 5:
				numerals += "D"
				c_count -= 5
		numerals += c_count * "C"
		num %= 100

		snake_case__	     =num // 10
		if x_count == 9:
				numerals += "XC"
				x_count -= 9
		elif x_count == 4:
				numerals += "XL"
				x_count -= 4
		if x_count >= 5:
				numerals += "L"
				x_count -= 5
		numerals += x_count * "X"
		num %= 10

		if num == 9:
				numerals += "IX"
				num -= 9
		elif num == 4:
				numerals += "IV"
				num -= 4
		if num >= 5:
				numerals += "V"
				num -= 5
		numerals += num * "I"

		return numerals






def 				a  (     UpperCamelCase_    :    str = "/p089_roman.txt"			)       ->	int:
		snake_case__	     =0

		with open(os.path.dirname(UpperCamelCase_			) + roman_numerals_filename			) as filea:
				snake_case__	     =filea.readlines()

		for line in lines:
				snake_case__	     =line.strip()
				snake_case__	     =parse_roman_numerals(UpperCamelCase_			)
				snake_case__	     =generate_roman_numerals(UpperCamelCase_			)
				savings += len(UpperCamelCase_			) - len(UpperCamelCase_			)

		return savings


if __name__ == "__main__":
			print(f"""{solution() = }""")




